Music listening is a very personal and situational behaviour, which suggests
that contextual information could be used to greatly enhance music recommendation
experience. However, making such use of mobile context, while learning user
profiles, is a challenging problem. This case study presents a system for
collecting context and usage data from mobile devices, but targeted at
recommending music via unsupervised learning of user profiles and relevant
situations. The developed data flow system supports both short enough response
times and longer asynchronous reasoning on the collected data; furthermore, the
mobile phone acts not only as sensor, but the mobile app is directly tied to the
effectiveness of the music service user experience (UX). This work describes our
system design and discusses issues related to the problem space and to usability
tests on such systems, based on an international user trial.